An Incremental Approach for Storage and Delivery Planning Problems

We consider a logistic planning problem for simultaneous optimization of the storage and the delivery. This problem arises in the consolidate shipment using an intermediate storage in a supply chain, which is typically found in the automobile industry. The vehicles deliver the items from the origin to the destination, while the items can be stored at some warehousing facilities as the intermediate storage during the delivery. The delivery plan is made for each day separately, but the storage at a warehouse may last for more than one day. Therefore, the entire logistic plan should be considered over a certain period for the total optimization. We formulate the storage and delivery problem as a mixed integer programming. Then, we propose a relax-and-fix type heuristic method, which incrementally fixes decision variables until all the variables are fixed to obtain a complete solution. Moreover, a semiapproximate model is introduced to effectively fix the variables. Based on the formulation, the delivery plan can be solved for each day separately. This has the advantage especially in the dynamic situation, where the delivery request is modified from the original request before the actual delivery day. Numerical experiments show that the simultaneous optimization gives the effective storage plan to reduce the total logistic cost, and the proposed heuristics efficiently reduce the computational time and are robust against the dynamic situation

Development of a Scheme and Tools to Construct a Standard Moth Brain for Neural Network Simulations

Understanding the neural mechanisms for sensing environmental information and controlling behavior in natural environments is a principal aim in neuroscience. One approach towards this goal is rebuilding neural systems by simulation. Despite their relatively simple brains compared with those of mammals, insects are capable of processing various sensory signals and generating adaptive behavior. Nevertheless, our global understanding at network system level is limited by experimental constraints. Simulations are very effective for investigating neural mechanisms when integrating both experimental data and hypotheses. However, it is still very difficult to construct a computational model at the whole brain level owing to the enormous number and complexity of the neurons. We focus on a unique behavior of the silkmoth to investigate neural mechanisms of sensory processing and behavioral control. Standard brains are used to consolidate experimental results and generate new insights through integration. In this study, we constructed a silkmoth standard brain and brain image, in which we registered segmented neuropil regions and neurons. Our original software tools for segmentation of neurons from confocal images, KNEWRiTE, and the registration module for segmented data, NeuroRegister, are shown to be very effective in neuronal registration for computational neuroscience studies

Elucidation of inhibitor-binding pocket of D-amino acid oxidase using docking simulation and N-sulfanylethylanilide-based labeling technology

Because of relevance of D-serine (D-Ser) to schizophrenia, inhibitors of D-amino acid oxidase (DAO), which catalyzes degradation of D-Ser in the presence of flavin adeninde dinucleotide (FAD), are expected as anti-schizophrenia therapeutics. In this study, binding pockets of DAO to its inhibitor 4-bromo-3-nitrobenzoic acid were searched by combining in silico docking simulation and labeling experiments employing an N-sulfanylethylanilide-based labeling technology we have developed. The results clearly demonstrated that there are two binding pockets: one is shared with D-Ser and FAD, and the other is an unexpected cleft between subunits of a DAO dimer. These findings will provide insight to aid the development of new hDAO inhibitors. In addition, it was also proved that our labeling technology could be applicable to elucidate the binding pockets of proteins

Computational Prediction of O-linked Glycosylation Sites That Preferentially Map on Intrinsically Disordered Regions of Extracellular Proteins

O-glycosylation of mammalian proteins is one of the important posttranslational modifications. We applied a support vector machine (SVM) to predict whether Ser or Thr is glycosylated, in order to elucidate the O-glycosylation mechanism. O-glycosylated sites were often found clustered along the sequence, whereas other sites were located sporadically. Therefore, we developed two types of SVMs for predicting clustered and isolated sites separately. We found that the amino acid composition was effective for predicting the clustered type, whereas the site-specific algorithm was effective for the isolated type. The highest prediction accuracy for the clustered type was 74%, while that for the isolated type was 79%. The existence frequency of amino acids around the O-glycosylation sites was different in the two types: namely, Pro, Val and Ala had high existence probabilities at each specific position relative to a glycosylation site, especially for the isolated type. Independent component analyses for the amino acid sequences around O-glycosylation sites showed the position-specific existences of the identified amino acids as independent components. The O-glycosylation sites were preferentially located within intrinsically disordered regions of extracellular proteins: particularly, more than 90% of the clustered O-GalNAc glycosylation sites were observed in intrinsically disordered regions. This feature could be the key for understanding the non-conservation property of O-glycosylation, and its role in functional diversity and structural stability

Blood Flow Restriction Increases the Neural Activation of the Knee Extensors During Very Low-Intensity Leg Extension Exercise in Cardiovascular Patients:A Pilot Study

Blood flow restriction (BFR) has the potential to augment muscle activation, which underlies strengthening and hypertrophic effects of exercise on skeletal muscle. We quantified the effects of BFR on muscle activation in the rectus femoris (RF), the vastus lateralis (VL), and the vastus medialis (VM) in concentric and eccentric contraction phases of low-intensity (10% and 20% of one repetition maximum) leg extension in seven cardiovascular patients who performed leg extension in four conditions: at 10% and 20% intensities with and without BFR. Each condition consisted of three sets of 30 trials with 30 s of rest between sets and 5 min of rest between conditions. Electromyographic activity (EMG) from RF, VL, and VM for 30 repetitions was divided into blocks of 10 trials and averaged for each block in each muscle. At 10% intensity, BFR increased EMG of all muscles across the three blocks in both concentric and eccentric contraction phases. At 20% intensity, EMG activity in response to BFR tended to not to increase further than what it was at 10% intensity. We concluded that very low 10% intensity exercise with BFR may maximize the benefits of BFR on muscle activation and minimize exercise burden on cardiovascular patients

Computational Prediction of O-linked Glycosylation Sites that Preferentially Map on Intrinsically Disordered Regions of Extracellular Proteins

O-glycosylation of mammalian proteins is one of the important posttranslational modifications. We applied a support vector machine (SVM) to predict whether Ser or Thr is glycosylated, in order to elucidate the O-glycosylation mechanism. O-glycosylated sites were often found clustered along the sequence, whereas other sites were located sporadically. Therefore, we developed two types of SVMs for predicting clustered and isolated sites separately. We found that the amino acid composition was effective for predicting the clustered type, whereas the site-specific algorithm was effective for the isolated type. The highest prediction accuracy for the clustered type was 74%, while that for the isolated type was 79%. The existence frequency of amino acids around the O-glycosylation sites was different in the two types: namely, Pro, Val and Ala had high existence probabilities at each specific position relative to a glycosylation site, especially for the isolated type. Independent component analyses for the amino acid sequences around O-glycosylation sites showed the position-specific existences of the identified amino acids as independent components. The O-glycosylation sites were preferentially located within intrinsically disordered regions of extracellular proteins: particularly, more than 90% of the clustered O-GalNAc glycosylation sites were observed in intrinsically disordered regions. This feature could be the key for understanding the non-conservation property of O-glycosylation, and its role in functional diversity and structural stability

Optimization in Supply Chain Management An Incremental Approach for Storage and Delivery Planning Problems

Abstract. We consider a logistic planning problem for simultaneous optimization of the storage and the delivery. This problem arises in the consolidate shipment using an intermediate storage in a supply chain, which is typically found in the automobile industry. The vehicles deliver the items from the origin to the destination, while the items can be stored at some warehousing facilities as the intermediate storage during the delivery. The delivery plan is made for each day separately, but the storage at a warehouse may last for more than one day. Therefore, the entire logistic plan should be considered over a certain period for the total optimization. We formulate the storage and delivery problem as a mixed integer programming. Then, we propose a relax-and-fix type heuristic method, which incrementally fixes decision variables until all the variables are fixed to obtain a complete solution. Moreover, a semiapproximate model is introduced to effectively fix the variables. Based on the formulation, the delivery plan can be solved for each day separately. This has the advantage especially in the dynamic situation, where the delivery request is modified from the original request before the actual delivery day. Numerical experiments show that the simultaneous optimization gives the effective storage plan to reduce the total logistic cost, and the proposed heuristics efficiently reduce the computational time and are robust against the dynamic situation

Evolutionary Computation for Dynamic Parameter Optimization of Evolving Connectionist Systems for On-line Prediction of Time Series with Changing Dynamics

Abstract- The paper describes a method of using evolutionary computation technique for parameter optimisation of evolving connectionist systems (ECOS) that operate in an on-line, life-long learning mode. ECOS evolve their structure and functionality from an incoming stream of data in either a supervised-, or/and in an unsupervised mode. The algorithm is illustrated on a case study of predicting a chaotic time-series that changes its dynamics over time. With the on-line parameter optimisation of ECOS, a faster adaptation and a better prediction is achieved. The method is practically applicable for real time applications